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library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.3     ✔ purrr   0.3.2
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ──────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ lubridate::as.difftime() masks base::as.difftime()
✖ dplyr::between()         masks data.table::between()
✖ tidyr::complete()        masks RCurl::complete()
✖ lubridate::date()        masks base::date()
✖ dplyr::filter()          masks plotly::filter(), stats::filter()
✖ dplyr::first()           masks data.table::first()
✖ lubridate::hour()        masks data.table::hour()
✖ lubridate::intersect()   masks base::intersect()
✖ lubridate::isoweek()     masks data.table::isoweek()
✖ dplyr::lag()             masks stats::lag()
✖ dplyr::last()            masks data.table::last()
✖ lubridate::mday()        masks data.table::mday()
✖ lubridate::minute()      masks data.table::minute()
✖ lubridate::month()       masks data.table::month()
✖ lubridate::quarter()     masks data.table::quarter()
✖ lubridate::second()      masks data.table::second()
✖ lubridate::setdiff()     masks base::setdiff()
✖ purrr::transpose()       masks data.table::transpose()
✖ lubridate::union()       masks base::union()
✖ lubridate::wday()        masks data.table::wday()
✖ lubridate::week()        masks data.table::week()
✖ lubridate::yday()        masks data.table::yday()
✖ lubridate::year()        masks data.table::year()

library(neonUtilities)
library(phenocamapi)
library(lubridate)
library(jpeg)
library(phenocamr)
library(plotly)

sites <- c("ONAQ", "HARV", "CPER", "OSBS")

## from tutorial https://www.neonscience.org/eddy-data-intro
# zipsByProduct(dpID="DP4.00200.001", package="basic", 
#               site=sites, 
#               #startdate="2018-06", enddate="2018-07",
#               savepath="/data", 
#               check.size=F)
# 
# flux_dpid <- "DP4.00200.001"
# flux <- stackEddy(filepath=paste0(getwd(), "/neonsummit/data/filesToStack00200"),
#                   level="dp04")

# phe_dpid <- 'DP1.10055.001'
# zipsByProduct(dpID='DP1.10055.001', package ="basic",
#               site=sites,
#               savepath="data",
#               check.size = F)
# 
# stackByTable(phe_dpid, filepath=paste0(getwd(), "/filesToStack10055"),
#              savepath = paste0(getwd(), "/filesToStack10055"), folder=T,
#              saveUnzippedFiles = FALSE)  #phe data
#### save locally, cannot push to git too big  #####
statusIntensity <- read.csv(paste0(getwd(),'/stackedFiles/phe_statusintensity.csv'),
                   stringsAsFactors = F, header = T)
perind <- read.csv(paste0(getwd(), '/stackedFiles/phe_perindividual.csv'),
                            stringsAsFactors = F, header = T)

#Format dates
statusIntensity$date <- as.Date(statusIntensity$date, "%Y-%m-%d")
statusIntensity$editedDate <- as.Date(statusIntensity$editedDate, "%Y-%m-%d")
statusIntensity$dayOfYear <- yday(statusIntensity$date)
statusIntensity$year <- substr(statusIntensity$date, 1, 4)
statusIntensity$monthDay <- format(statusIntensity$date, format="%m-%d")

#remove duplicate records
statusIntensity <- select(statusIntensity, -uid)
statusIntensity <- distinct(statusIntensity)
si_last <- statusIntensity %>%
  group_by(individualID, date, phenophaseName) %>%
  filter(editedDate==max(editedDate))

perind <- select(perind, individualID, growthForm, taxonID, editedDate)
perind <- distinct(perind)
ind_last <- perind %>%
  group_by(individualID) %>%
  filter(editedDate==max(editedDate))
ind_last <- select(ind_last, -editedDate)
si_last <- left_join(si_last, ind_last)

Joining, by = "individualID"

df <- filter(si_last, 
             siteID%in%c('HARV', 'OSBS', 'ONAQ')
             & year>2015
             & phenophaseStatus=='yes' 
             #& taxonID%in%c('QUFA', 'QULA2')
             #& growthForm=='Deciduous broadleaf'
             & phenophaseName%in%c('Breaking leaf buds', 'Initial growth', 'Emerging needles') 
             #& phenophaseIntensity !=""
)
df_out <- df%>%
  group_by(siteID, taxonID, date)%>%
  summarise(count=n())%>%
  arrange(date)
ggplot(df_out, aes(x=date, y=count)) +
                   #, group=taxonID)) +
  geom_line(aes(color=siteID), size=1)+
  ylim(0, 30)+
  facet_grid(siteID ~ .)
ggsave('SpringPhe_inSitu.png')

Saving 7.1 x 4.39 in image

p = plot_ly() %>%
  add_trace(
  data = df_out[df_out$siteID=='HARV',],
  x = ~ date,
  y = ~ count,
  name = 'HARV',
  showlegend = TRUE,
  type = 'scatter',
  mode = 'marker'
)
p2 =  plot_ly() %>%
  add_trace(
  data = df_out[df_out$siteID=='OSBS',],
  x = ~ date,
  y = ~ count,
  name = 'OSBS',
  showlegend = TRUE,
  type = 'scatter',
  mode = 'marker'
)
p3 = plot_ly() %>%
  add_trace(
  data = df_out[df_out$siteID=='ONAQ',],
  x = ~ date,
  y = ~ count,
  name = 'ONAQ',
  showlegend = TRUE,
  type = 'scatter',
  mode = 'marker'
)
p_all <- subplot(p, p2, p3, nrows = 3)
p_all

## can't figure out how to save as .png ###